The previous chapters have begun to examine how hype is created, circulated, and increasingly institutionalised within the digital economy. We now shift our focus from producing hype to navigating, interpreting, and evaluating it. This chapter demonstrates that in an era of continual disruption, decision-makers are pressured to treat hype not as mere noise but as a strategic factor – one that demands deliberate evaluation, careful timing, and active management. We begin by illustrating the stakes through historical and contemporary examples, then examine the challenges posed by uncertainty in innovation, and conclude by showing how organisations are responding through emerging hype-evaluation practices. We trace the rise of hype’s new experts – most notably industry analysts – tasked with helping organisations separate genuinely promising innovations from overblown claims.

This chapter, therefore, turns to the organisational decision-makers who must make sense of, and act within, an environment saturated with competing and exaggerated technological narratives. An unprecedented surge in digital innovations – particularly those billed as radical or disruptive – means that organisations face a dual challenge: spotting genuine opportunities while mitigating the risks of chasing inflated promises.

Consider a start-up like Juvo, which claims to disrupt traditional banking models. The instinctive reaction might be to dismiss such rhetoric outright. Yet, as argued here, decision-makers can no longer afford to ignore such claims. Hyped innovations have become a strategic variable with potentially life-or-death consequences for firms (Arnold et al., Reference Arnold, Breitenmoser, Röth and Spieth2022).

A historical example illustrates how organisational responses to hype have evolved. One of the authors conducted research in the 1980s on the financial sector (Fincham et al., Reference Fincham, Fleck, Procter, Scarbrough, Tierney and Williams1995), finding that banks at the time underestimated the disruptive potential of automated teller machines (ATMs). Professional bankers of that era dismissed predictions of significant industry change as speculative and unrealistic. As their terminology reveals, they saw ATMs as mere extensions of the teller function – a means of improving efficiency rather than a transformative innovation. Yet within a decade, ATMs had not only proliferated but also paved the way for online banking – a transformation those bankers failed to foresee. This perspective reflected a wider mindset of the time, which treated computing technologies as incremental aids rather than as drivers of fundamental change. Indeed, ATMs – initially introduced simply as an ‘automatic’ teller – were not recognised as precursors to a major industry transformation (see Locatelli et al., Reference Locatelli, Schena, Tanda, King, Stentella Lopes, Srivastav and Williams2021).

These historical missteps highlight the risks associated with discounting technological hype while underestimating the transformative potential of emerging innovations. Juvo’s ambitious assertions may indeed be inflated, yet the possibility remains that they could catalyse significant disruption in the banking sector. This highlights a recurrent organisational dilemma: dismissing hype risks forgoing genuine opportunities for innovation while embracing it risks committing resources to ultimately insubstantial promises. Compounding this dilemma is a further contemporary challenge: organisations must increasingly navigate a proliferation of overlapping and competing promissory narratives – each pitched as a strategic opportunity or threat – without becoming paralysed or distracted.

3.1 The Timing of Innovation Responses

Today’s landscape demands a fundamentally different approach to innovation. Kumaraswamy et al. (Reference Kumaraswamy, Garud and Ansari2018, p. 1026) observe that the twenty-first century is characterised by ‘continual disruption in which technological innovations and new business model changes affect not just individual firms, but entire industries and ecosystems’. In this environment, understanding and responding to hype – in essence, managing the timing of innovation responses – has become a crucial issue.

Innovation has long been recognised as important for an organisation to survive in an increasingly competitive market; however, its role is now seen as far more strategic. In the past, innovation was viewed primarily as a way to sustain productivity and competitiveness (Godin, Reference Godin2015). Today, however, it is increasingly seen as a strategic matter because of its potential to transform the industrial landscape and radically restructure the position of players within that landscape. Incumbent organisations may find themselves at risk of being displaced by challengers wielding radical new technologies (Freeman, Reference Freeman1994) or disruptive business models (Christensen, Reference Christensen1997), generating what Schumpeter (Reference Schumpeter1942) characterised as ‘gales of creative destruction’. Conversely, these developments open up opportunities for challengers like Juvo, which can ride these waves to achieve rapid growth, higher profits, and perhaps ultimately exclude rivals in winner-takes-all competitions (Palmié et al., Reference Palmié, Wincent, Parida and Caglar2020). The stakes are thus rising significantly.

This shift in focus to the strategic impact of innovation has coincided with a dramatic increase in the pace and dynamism of technological change. These trends further amplify the challenges that innovation poses to decision-makers. The field of Innovation Studies (Godin, Reference Godin2015) emerged in the twentieth century, examining how major technological advances, such as steam power and electric motors, gradually worked their way through the economy, patterns reflected in generation-long Kondratiev waves (Perez, Reference Perez2015). By contrast, the current era of digital innovation features vastly higher rates of development and uptake of new products.

Together with shorter product life cycles, this acceleration generates massive market turbulence (Perez, Reference Perez2015). Large multidivisional firms with dedicated R&D departments – once the powerhouses of innovation in the nineteenth and twentieth centuries – have now been outpaced by a proliferation of smaller, newer players, particularly in digital innovation (Menz et al., Reference Menz, Kunisch, Birkinshaw, Collis, Foss, Hoskisson and Prescott2021). There is, in consequence, a multiplication in the number of voices articulating claims about a rapidly growing array of novel solutions and the benefits these will bring (Yoo et al., Reference Yoo, Boland, Lyytinen and Majchrzak2012).

This accelerating flood of digital innovations – especially radical or disruptive ones – presents both opportunities and challenges for organisational managers and other market actors. The need to respond to potentially disruptive changes brings this dilemma to the forefront: How can managers navigate and evaluate these competing claims? As we will explore later, claims of novelty inherently – and often deliberately – generate uncertainty (Jalonen, Reference Jalonen2012). It is in this context that the term ‘hype’ first emerged to highlight the risk (indeed, the likelihood) that vendor claims may be unrealistic. A vendor might behave opportunistically (Williamson, Reference Williamson1975), exaggerating potential benefits or underestimating the difficulties of achieving them.

Traditional business wisdom has emphasised the risks of failure associated with promising innovations, concluding that it is often best to delay adoption until the prospects are more clearly established (Khanagha et al., Reference Khanagha, Ramezan Zadeh, Mihalache and Volberda2018). However, as hype’s strategic significance grows, so does the need for structured approaches to evaluate and respond to it. In the following sections, we examine emerging frameworks for navigating the uncertainties inherent in hype-driven innovation.

3.2 Innovation Dilemma: The Used Apple Policy

Organisations face a profound tension in navigating technological innovation: the need to act early to seize opportunities versus the risk of committing to unproven solutions. This paradox – the innovation dilemma – requires balancing urgency with caution, particularly in today’s rapidly evolving digital economy.

Theodore Levitt’s (Reference Levitt1965) classic paper in Harvard Business Review highlights the high costs, frequent failure and consequently deeply uncertain returns of new product developments. In a period in which the innovation literature focused on the maturation of product cycles of successful products, Levitt notes that ‘most new products don’t have any sort of classical life cycle curve at all’. Instead, ‘from the very outset’, they have ‘an infinitely descending curve’. The ‘product not only doesn’t get off the ground; it goes quickly underground – six feet under’ (Levitt, Reference Levitt1965, p. 82).

These costs and risks might be presumed to inhibit innovation altogether. However, Levitt goes on to propose a different strategy that ‘badly burned’ organisations have developed that he calls the ‘used apple policy’:

Levitt’s observation highlights the dilemma confronting organisational managers seeking to minimise risks and maximise benefits in uncertain technology markets. Innovation is highly uncertain. High costs and risks of failure can offset potentially high returns. These uncertainties vary substantially over the life cycle of an innovation. Uncertainties are highest in the early stages of development and adoption of a technology (Rosenberg, Reference Rosenberg2009). Levitt warns of the risks faced by early adopters, arguing that being first can be perilous. Rosenberg (Reference Rosenberg1976) similarly wrote of ‘anticipatory retardation’ to describe situations where firms delay adoption due to expectations that improved versions of a technology are imminent. Thus, delaying adoption may be sensible. However, where technology creates strategic market transformation shifts rather than merely improving productivity, the difference between getting the first and last bite of the apple matters hugely (Geels & Smit, Reference Geels and Smit2000).

Timing (concerning a technology’s lifecycle) matters. It seems safer to delay investing in an unproven innovation. However, though uncertainty and the risk of expensive failures are highest at the early stages of a novel technology, so too are the potential benefits. Early adopters may secure a competitive advantage before this is eroded by the wide availability of equivalent functionality as the market matures (Ward, Reference Ward1987). Entrepreneurs and investors may derive greatly enhanced profits from their temporary monopoly of exploitation of novel innovations (Schumpeter, Reference Schumpeter1942), which sweep through industries in a ‘wave of creative destruction’. Today, attention focuses on the opportunity for disruptive innovators to displace existing incumbents and capture new market territory through new platform technologies and business models (Bower & Christensen, Reference Bower and Christensen1995).

Levitt’s early contribution thus introduces some of the critical issues in our investigation into hype. The most significant opportunities for profit and growth arise in precisely the period when uncertainties are at their highest (Knight, Reference Knight1921). Those wanting to share the benefits of early adoption and avoid being displaced by challengers like Juvo are called on to invest when reliable information to inform a decision is least available. This is the paradox at the heart of our enquiry. And it is growing more acute in the era of digital innovation, as the rate of new digital innovation increases, accompanied by shorter software development cycles and more rapid maturation and obsolescence (Nylén & Holmström, Reference Nylén and Holmström2015). Platform innovations and other radical and disruptive innovations have gained huge salience with their promise to displace incumbents and deliver market share and profit to early investors and adopters (Gawer, Reference Gawer2011).

Adopters must invest in opportunities before verifiable evidence becomes available that the technologies will be productive (Spieth et al., Reference Spieth, Röth, Clauss and Klos2021). This means the capability to gauge the plausibility of hype becomes critical. To avoid what Kumaraswamy et al. (Reference Kumaraswamy, Garud and Ansari2018) characterise as ‘errors of commission’, traditional business wisdom emphasises the risks of failure for promising innovations and concludes that it is often best to delay adoption until the prospects are more clearly established. However, the temporality of innovation responses has become a crucial factor.

Delaying a response until robust evidence emerges of the prospects of a promising technology may reduce the risk of costly investment in unsuccessful technology pathways. However, it creates new risks from delayed access to expected benefits, and more crucially, from missing out on new opportunities for first movers from radical and disruptive change and the elevated rewards they may bring for investors and adopters. In this scenario, catch-up strategies may be expensive and miss out on the most significant rewards – characterised by Kumaraswamy et al. (Reference Kumaraswamy, Garud and Ansari2018) as ‘errors of omission’.

Timing is also vital because these complex digital and organisational innovations are not ‘plug and play’ solutions offering readily identifiable and achievable benefits (Pollock & Williams, Reference Pollock and Williams2008). Adopters may need to invest significant attention and money to assess novel, perhaps arcane, technological fields (Schot & Geels, Reference Schot and Geels2008). They may also need to acquire the necessary expertise and intelligence to appraise and exploit/appropriate them.

Considerable work may be required to get a new offering operating effectively in its intended context, and over time, to optimise performance (Fleck, Reference Fleck1994). Organisational managers may also need to unlearn entrenched views of how technology may be utilised. Acquiring technologies and developing responses may be delayed by competition between organisational teams wedded to existing and novel approaches (Volberda et al., Reference Volberda, Khanagha, Baden-Fuller, Mihalache and Birkinshaw2021).

To make timely decisions, decision-makers must, in the words of Kumaraswamy et al. (Reference Kumaraswamy, Garud and Ansari2018, p. 1030), ‘cultivate the capacity to read weak signals about potentially disruptive innovations and explore options before it is too late’. But one effect of the ‘continual disruption’ (Kumaraswamy et al. Reference Kumaraswamy, Garud and Ansari2018) described above is even greater uncertainty for those navigating the sea of hype.

3.3 Uncertainty and Innovation

Uncertainty is not just an incidental (if unwelcome) by-product of innovation. Innovation inherently generates uncertainty, disrupting traditional decision-making frameworks. However, this uncertainty is not merely a hurdle; it also drives economic dynamism, pushing organisations to develop new strategies for navigating risk and opportunity (Dorobat et al., Reference Dorobat, McCaffrey, Foss and Klein2025).

This paradox of innovation under uncertainty has long been recognised in economic thought. For instance, Knight’s (Reference Knight1921) classic text argues that profit depends on imperfect knowledge and indeed that profit ‘would not arise’ under conditions of certainty (Knight, Reference Knight1921, p. 198). Knight differentiates between risk, which involves calculable probabilities (such as market volatility), and uncertainty, where the combinatorial complexity of economic life and countless unknown factors make probabilities incalculable within business decision timeframes. His account marks a sharp departure from neo-classical models of markets composed of rational actors with perfect information. He proposes that imperfect knowledge and asymmetrical access to information are crucial for profit and even play a generative role in the economy. In short, because the future cannot be calculated in advance, decision-makers must rely on judgement and storytelling – which is exactly the space where hype operates.

Keynes (Reference Keynes1936) focused on decision-making under uncertainty and introduced the concept of ‘animal spirits’ to describe the instincts, sentiments, and spontaneous urges that shape economic behaviour when rational calculation reaches its limits. For Keynes, in conditions where outcomes cannot be known by ‘quantitative probabilities’, investment decisions are not made purely through ‘mathematical expectation’ but are instead driven by confidence, mood, and social cues (Keynes, Reference Keynes1936, cited in Dow & Dow, Reference Dow and Dow2012). In other words, he shows how exaggerated optimism or pessimism can drive decisions when evidence is lacking. As Keynes sees it, these animal spirits, which include emotions like confidence, optimism, pessimism, and fear, are not irrational; rather, they are necessary responses to radical uncertainty – ‘of a spontaneous urge to action rather than inaction’ (1936, p. 161) – arising in the absence of stable expectations. Keynes’ perspective further challenges the image of the rational economic actor, underscoring how markets are propelled not just by information and analysis but by shifting waves of confidence and belief (Akerlof & Shiller, Reference Akerlof and Shiller2010).

Schumpeter (Reference Schumpeter1947, p. 151) further develops this point, arguing that innovation is the primary driver of growth in capitalist societies. His evolutionary economic account revolves around the entrepreneur/innovator, willing to take a risk, and in return secure enhanced rent from monopoly exploitation of radical innovations by ‘the doing of new things or the doing of things that are already being done in a new way’. For Schumpeter (Reference Schumpeter1912, p. 163), innovation involves recombination: ‘innovation combines components in a new way’, with entrepreneurs using their ‘more acute intelligence and a more active imagination’ to envisage ‘countless new combinations’.

In sum, these classic perspectives show that because the future cannot be calculated, judgement and persuasive narratives become crucial in innovation.

Building on these ideas, modern innovation economists note that radical innovations carry especially high uncertainty. Freeman (Reference Freeman1974, p. 226) pointed out that radical innovations demand major shifts in skills and often bring in new players, thus entailing a ‘very high degree of uncertainty’. Abernathy and Clark (Reference Abernathy and Clark1985) identified four innovation types based on whether an innovation conserves or disrupts existing competences and linkages. Particularly notable are architectural innovations (which disrupt competences and linkages) and revolutionary innovations (which disrupt competences but conserve linkages). This typology highlights that innovation is an inherently uncertain and combinatorial process, often requiring the creation of new knowledge and networks.

These were just the beginning of a growing body of work aimed at highlighting and classifying forms of innovation that diverge from existing technological and business models. A substantial body of literature has developed taxonomies to differentiate forms of innovation (Godin, Reference Godin2015). Though little agreement exists about the relationship between these frameworks, they exhibit homologies (Edwards-Schachter, Reference Edwards-Schachter2018). Many of these taxonomic efforts have focused on discontinuities in innovation (Breschi et al., Reference Breschi, Malerba and Orsenigo2000), which are variously conceived as a corollary to periods of stability. These accounts start with the observation that most innovation involves ‘incremental’ (Freeman, Reference Freeman1974) changes to existing technologies and processes in which the innovation ‘trajectory’ follows an established ‘paradigm’ (Dosi, Reference Dosi1982) or ‘regime’ (Nelson & Winter, Reference Nelson and Winter1982) patterned by broadly shared knowledge, design heuristics, standards, and regulations within a sector.

This emphasis on periods of stability also accentuates periodic shifts in technological paradigm (Constant, Reference Constant1973; Dosi, Reference Dosi1982). The factors creating discontinuous forms of innovation have been labelled variously in successive accounts as radical (Freeman, Reference Freeman1974), revolutionary (Constant, Reference Constant1973; Abernathy & Clark, Reference Abernathy and Clark1985), disruptive (Christensen, Reference Christensen1997), or emerging (Ho & Lee, Reference Ho and Lee2015; Rotolo et al., Reference Rotolo, Hicks and Martin2015). Though different authors use varying terms – radical, revolutionary, disruptive, emerging – these frameworks all point to the disruptive consequences of discontinuities in innovation (Constant, Reference Constant1973; Dosi, Reference Dosi1982; Christensen, Reference Christensen1997; Rotolo et al., Reference Rotolo, Hicks and Martin2015). These paradigm shifts, variously conceived, all appear to involve changes both in the knowledge and understanding required and in relationships between actors (Abernathy & Clark, 1995), changes which pose deep uncertainties for the players involved.

This recurring emphasis on discontinuities in innovation raises questions about existing knowledge, suggesting that prior understandings may no longer be valid. Existing competences, routines, and evaluation criteria for technology design and business models are called into question (McBride et al., Reference McBride, Packard and Clark2024). New understandings of the operation of a promising technology and its potential uses and users may need to be developed. The requirement for new kinds and combinations of knowledge may call for the inclusion of additional players and changes in the relationships between them (Breschi et al., Reference Breschi, Malerba and Orsenigo2000).

Uncertainty is not merely incidental but intrinsic to the claim of novelty that lies at the heart of innovation. Moreover, the claim to novelty by those vendors developing or promoting new innovations purposefully generates radical uncertainty. According to Joly (Reference Joly, Akrich, Barthe, Muniesa and Mustar2010), this is intentional, as innovators must show how their ideas vastly differ from what came before to attract support and mobilise resources. This insight helps explain why hyped expectations around emerging technologies tend to unfold, as Joly (Reference Joly, Akrich, Barthe, Muniesa and Mustar2010, p. 14) sees it, in predictable ways: the ‘technology is presented as brand new’ where ‘it will create a new society’. However, by stressing this dramatic break from established methods, innovators and entrepreneurs inadvertently introduce profound uncertainties about these technologies as they declare existing evaluation criteria obsolete, and traditional metrics and assessment methods become less relevant in this context (see Tenet Three, which discusses how hype asserts the obsolescence of established evaluation criteria).

This generates a profound paradox for decision-makers. When innovation departs from known paradigms, traditional ways of understanding, and evaluating technologies – rooted in extrapolating from past performance – break down. How can organisations evaluate novel capabilities if old models no longer apply?

3.4 Evaluating Radical Futures

Given this paradox, the uncertainties surrounding radical innovation challenge traditional ideas of decision-making as a rational calculation among finite alternatives. Knight places at the core of his economic model what he called a ‘theory of knowledge’ (Knight, Reference Knight1921, p. xii). He observed that the process of retrospectively classifying events with similar behaviours – the hallmark of ‘mechanistic science’ – cannot be applied to typical business decisions, which ‘deal with situations which are far too unique, generally speaking, for any sort of statistical tabulation to have any value for guidance’ (Knight, Reference Knight1921, p. 231).

Knight’s (Reference Knight1921, p. 209) theory of knowledge closely follows the traditional positivist model of science, describing it as a ‘theory of exact knowledge, of rigorous demonstration’. Yet he shows that this model becomes unworkable in everyday economic life, where constant change undermines the stability required for such knowledge to function. As he notes, ‘the properties of things and their relationships are constantly changing’ (Knight, Reference Knight1921, p. 207). As a result, profound uncertainty – what we now term Knightian uncertainty – arises. When there is ‘real change’ – for example, when considering emerging futures – ‘it seems clear that reasoning is impossible’ (Knight, Reference Knight1921, p. 209). Put simply, Knight argues that the future cannot be known through statistical generalisation, as each business situation is irreducibly unique. Therefore, economic actors must make decisions in the absence of calculable probabilities, and it is this uncertainty that gives rise to the possibility of profit.

This insight casts doubt on the possibility of knowing technological futures in any definitive sense. Clardy (Reference Clardy2022, p. 1) similarly argues that studies of the future cannot produce ‘knowledge’ in the strict epistemological sense, because ‘their actual truth values cannot be determined until some later time’. Thus, claims about the future cannot be verified or falsified in advance. Futures studies, therefore, grapple with a fundamental epistemic limitation.

Adam (Reference Adam2023, p. 280) echoes this position by arguing that the ‘future is not yet and as such cannot be considered factual’. Mirroring Knight’s (Reference Knight1921) critique of mechanistic science, Adam notes that ‘in the scientific mode of enquiry, the future per se is actually dis-attended’. She writes that while it is ‘possible to produce probability calculations, predictions and models of the future, which are compatible with scientific methods of enquiry’, this ‘way of knowing the future is rooted in past and present actions or events, its results are inescapably past-based simulations, masquerading as “knowledge” of a future – at best imperfect’ (Adam, Reference Adam2023, p. 280). In other words, scientific approaches project the future based on historical patterns, but this projection is always provisional and incomplete.

Adam (Reference Adam2023, p. 280) further notes that the temporal orientation of social science and economics is largely retrospective: ‘The temporal orientation of scientific investigation of this social world is focused primarily on completed (past) acts or (present) reported anticipations’. She writes (2023, p. 281) that when ‘politicians, policymakers, economists and teachers, for example, want to know what lies ahead, they rely on spatial and material knowledge of the past, from which they extrapolate what might be’.

However, there are contrasting accounts that challenge the idea that the past entirely constrains the future. Some scholars, on the one hand, emphasise the future’s open-ended indeterminacy. For instance, Selin (Reference Selin2008, p. 1888) introduces the notion of the ‘ontological indeterminacy of the future’, suggesting that we are ‘actively creating and re-creating multiple futures’. Similarly, Köhler et al. (Reference Köhler, Geels, Kern, Markard, Onsongo, Wieczorek and Wells2019, p. 3) emphasise that the future is ‘open-ended’ and that it is ‘impossible to predict which [promising innovations] will prevail’. These perspectives highlight the radically contingent nature of the future but can risk presenting it as a vague or undifferentiated cloud of possibilities.

On the other hand, scholars argue that the future is not a blank slate. For instance, Halford and Southerton (Reference Halford and Southerton2023, p. 273) argue that the future is ‘not empty’, open to ‘any kind of hopes or possibilities’ but is ‘already here’ – shaped by ‘latent materialities’ such as infrastructures, investments, and institutional legacies. These pre-existing elements condition the shape of plausible futures. In their view, actors are not operating on a blank canvas but are constantly navigating futures already partly determined by historical and material constraints.

Rather than seeing the unknowability of the future as a dead end, other scholars (Tutton, Reference Tutton2017) focus on how future imaginaries function in the present. For example, while acknowledging that the facticity of future claims cannot be verified, Tutton (Reference Tutton2017) argues that propositions about the future can still be studied in terms of their effects. Drawing on Bell and Mau (Reference Bell and Mau1971), he suggests that the future is real ‘to the extent to which present alternatives or possibilities for the future are real’, and that we can understand futures by analysing the ‘images of the future’ that guide present action (Tutton, Reference Tutton2017, p. 481). That is, imagined futures have a performative role and can be empirically examined as social facts. We can study how actors navigate and evaluate unpredictable technology futures as routine practice. In other words, despite the limits to knowing the future, actors and organisations are continually engaging in future-oriented practices. How does this play out in everyday business decision-making?

3.5 An Economy Obsessed with the Future

According to Joly (Reference Joly, Akrich, Barthe, Muniesa and Mustar2010), there is an increasing orientation of market actors towards the future (see also Wenzel et al., Reference Wenzel, Cabantous and Koch2025). If this is the case, how do market actors routinely assess and steer their way through the shifting terrain of plausible futures? Despite the uncertainties, actors routinely engage in practical, everyday activities to navigate and evaluate the future.

Halford and Southerton (Reference Halford and Southerton2023, p. 274) argue that researchers must ‘explicitly recognise [actors’] capacity to engage directly in future-making practice’. By future-making practice, they mean the ‘doings and sayings of a diverse range of actants actively engaging in claiming what futures might and should be, and in materialising these claims’ (Halford & Southerton, Reference Halford and Southerton2023, p. 274). In this view, organisations are not merely responding to a pre-given future but are actively shaping it through their expectations, plans, and investments.

Adam (Reference Adam2023) offers a complementary perspective, suggesting that much of daily life is conducted with a view to the future. As she puts it:

Bazzani (Reference Bazzani2023, p. 384) also explores this idea through the concept of ‘practical consciousness’, which enables actors to navigate the future without explicit deliberation. He describes it as the tacit knowledge that allows individuals to ‘go on in the contexts of social life without being able to give them direct discursive expression’ (Bazzani, Reference Bazzani2023, p. 384). This form of engagement, he adds, often involves the deployment of ‘routines’, which are ‘unreflective flows of activities in which habits do all the perceiving, recalling, judging, conceiving and reasoning that is done’.

Thompson and Byrne (Reference Thompson and Byrne2022) highlight the importance of constructing plausible visions of the future. They suggest that market actors develop practical knowledge distinct from scientific knowledge – a distinction they argue warrants further exploration. As they ‘cannot act solely by identifying optimal choices based on past statistical information’, they ‘create and use imagined futures to attend to questions of possibility rather than epistemology’ (Thompson & Byrne Reference Thompson and Byrne2022, p. 248). It is these ‘imagined futures’ that help orient present actions. As Thompson and Byrne (Reference Thompson and Byrne2022, p. 248) explain:

These ideas closely align with Beckert’s (Reference Beckert2016, Reference Beckert2021) influential concept of fictional expectations. Beckert (Reference Beckert2021, p. 2) notes that ‘organisations respond to the question of how to handle the future … by creating imaginaries of the future as “placeholders” … allowing them to make sense of the future and to act “as if” the future would unfold in a specific way’.

Together, these perspectives suggest that while the future remains fundamentally unknowable, it is not ungovernable. Moreover, it also points to how organisations are not paralysed by uncertainty. Despite the epistemic challenges, modern organisations are far from passive – in fact, they are increasingly future-oriented in their day-to-day operations. How, then, do they go about envisioning and assessing the future? Market actors deploy a range of tools, imaginaries, and practices – explicit and tacit – to make the future actionable. The task is not to predict the future with certainty but to engage with it through strategic and situated forms of judgement (Wenzel et al., Reference Wenzel, Cabantous and Koch2025).

Crucially, Beckert goes further by arguing that actors not only navigate uncertainty through imaginative projection but also develop criteria for distinguishing more plausible futures from less plausible ones. Although fictional expectations cannot be verified in advance, he argues, it is clear that they can be evaluated for plausibility: ‘They can be deemed credible, but their actual accuracy cannot be known’ (2021, p. 4). For Beckert (Reference Beckert2021, p. 4), fictional expectations do not mean ‘fanciful fantasies, but rather assessments of future developments that combine known facts with assumptions, informed judgements and emotions’. He goes on,

However, he notes that their ‘broken relationship to reality’ (Beckert, Reference Beckert2013, p. 225), which is an inherent characteristic of economic predictions under uncertainty: ‘In Economics, people evaluate predictions not just on how compelling their narrative is but also on their practical credibility. This is because the future reality simply cannot be known in the present’ (Beckert, Reference Beckert2021, p. 4, emphasis in original).

Beckert’s choice of the term ‘fictional’ is doubtless chosen for its rhetorical value in displacing entrenched ideas of economic decision-making based on rational calculation. By highlighting the narrative creativity underpinning economic projections, he draws attention to the performative and imaginative dimensions of markets. The fiction terminology is potentially unhelpful as it implies an openness about articulating fictional claims.

Beckert himself acknowledges the risk of the term ‘fictional’ being misunderstood. In a later paper, he warns that departing from a rational-planning perspective could send us ‘down the rabbit hole’ of treating all knowledge as fiction (Beckert, Reference Beckert2021, p. 6). A way to circumvent this, he argues (Beckert, Reference Beckert2021, p. 6), is to distinguish between ‘types of situations’. This seems important for our analysis of hype. He writes:

In other words, Beckert invites us to differentiate between fictional expectations based on the degree of empirical grounding that is possible. In early-stage innovation, projections often serve a symbolic role – what he calls ‘rationality badges’ – providing an appearance of rigour even when mainly based on assumptions. Such predictions may involve ‘mystical numbers’, suggesting spurious precision. However, as innovations mature, expectations should be held to higher standards of evidence and credibility. Beckert (Reference Beckert2021) thus reminds us that claims about the future are subject to varying forms of accountability. As he (Reference Beckert2016, p. 177) sees it, the early phase of innovation is ‘particularly prone to myths’, but the later stage claims less so, as they are subject to more accountability.

Logue and Grimes (Reference Logue and Grimes2022) make a similar point. They suggest that hype performs different roles over time and is subject to shifting forms of accountability: ‘Inasmuch as hype presents entrepreneurs with both a resource for motivating early engagement and also a relational liability for sustaining such engagement, it is essential that new ventures understand how to manage hype over time’ (Logue & Grimes, Reference Logue and Grimes2022, p. 1078).

These insights are directly relevant to our analysis of hype. While often dismissed wholesale as misleading or deceptive (Vinsel & Russell, Reference Vinsel and Russell2020), the work of scholars such as Beckert (Reference Beckert2016, Reference Beckert2021) and Logue and Grimes (Reference Logue and Grimes2022) offers a foundation for distinguishing between different forms and contexts. As our book argues, hyped expectations vary in their plausibility – some are grounded in technical feasibility and accumulated expertise, while others rest on hopeful speculation or tenuous assumptions.

3.6 The Business of Hype

In the digital economy, the task of navigating and evaluating hype has shifted from being an in-house, ad hoc activity to the basis of a specialist business – and a big one at that. Today, navigating hype is itself outsourced to specialist experts – industry analysts and analyst relations (AR) professionals – indicating that interpreting hype has become a commodified business function. Indeed, it could be argued that hype mobilisation and evaluation is now an industry in its own right – traded through consulting services and subscription-based research products. What was once an informal narrative practice – undertaken by firms to generate interest and secure resources – has evolved into a structured business populated by expert actors.

Economic sociologist Mützel (Reference Mützel2022) describes this as a ‘market of expectations’, where gatekeepers actively create and trade in stories – expectations, projections, and imaginings – about the future. It bears emphasising that hype’s new actors profit from the very excitement they help stage-manage. Analyst firms derive substantial revenues from selling (hype) evaluations and advice, while technology vendors pay AR professionals and agencies to boost their (hype) visibility. In other words, hype has become a monetised service. The fervour around future technologies is not just a cultural phenomenon but a commercial commodity – one that is packaged, traded, and strategically modulated by expert firms in the business of hype.

In our previous research, we identified how this business of expectations was not accidental or unstructured but orchestrated by promissory organisations (Pollock & Williams, Reference Pollock and Williams2010, Reference Pollock and Williams2016) – organisations whose core function is to craft, manage, and circulate future-oriented expectations. Among the most prominent examples of such organisations are industry analyst firms, though they are by no means the only ones (see Beckert, Reference Beckert2021). What we want to do here is examine more closely how these firms produce what we call ‘promissory products’.

Drawing on Espeland and Stevens (Reference Espeland and Stevens2008), we define promissory products as mechanisms for rendering hype visible and actionable – by checking, visualising, commensurating, and quantifying it. They are tools that allow technology adopters and investors to make sense of competing claims without having to vet each one individually. Whereas in the dotcom era, a technology buyer might have had to personally assess the credibility of dozens of start-up claims (Garud et al., Reference Garud, Gehman and Giuliani2014), today – confronted with potentially an order of magnitude more claims – they are more likely to rely on promissory products such as the Gartner Hype Cycle Chart (HCC), Magic Quadrant, or something similar. These tools serve to pre-filter the deluge of expectations, effectively outsourcing the initial sorting and prioritisation of hype to specialised experts.

We previously explored how industry analysts arose in response to fundamental uncertainties in the supply of off-the-shelf software systems in the market (Pollock & Williams, Reference Pollock and Williams2016). These uncertainties centred on a solution’s fit with a technology adopter organisation’s specific requirement and working methods, alongside questions about software providers’ capabilities to deliver on promises. Since these factors could not be established through simple inspection or a comparative assessment of vendors within the technology field, industry analysts introduced tools, such as the Magic Quadrant ranking, to help organisational managers make procurement decisions (Pollock & Williams, Reference Pollock and Williams2016).

However, the needs of industry analysts’ core clients – primarily technology adopters – have evolved significantly since the 2010s. Driven by dramatic changes in digital innovation as discussed above (Nambisan, Reference Nambisan2017), today’s adopters must look beyond mature products to emerging innovations that are not yet fully developed or tested. This shift has created two crucial requirements:

These developments have increased the difficulty of technology appraisal, particularly for managers in adopter organisations. Unlike venture capital (VC) investors, who can specialise in specific domains and make concentrated bets (Pflueger & Mouritsen, 2024), organisational technology adopters must maintain a broad perspective. They need to monitor a wide portfolio of developments, often without the resources or incentives to conduct in-depth investigations. As a result, many organisations outsource these anticipatory functions to industry analysts, consultants, and other types of futurists (Mangnus et al., Reference Mangnus, Oomen, Vervoort and Hajer2021).

Industry analysts have responded strategically to these changing demands, evolving from guiding buyers through existing markets to helping clients navigate the uncertainties of emerging technologies. Their analysts employ a ‘T-shaped’ distribution of expertise, combining in-depth knowledge of specific domains with a broad understanding of developments across the digital economy (Pollock & Williams, Reference Pollock and Williams2016). This capacity underpins the development of new products, such as the HCC and Cool Vendor designation, that target proto-fields and embryonic markets.

Our book examines the origins and motivation of various promissory products and the epistemic system surrounding their production and maintenance. We will focus on four key types of promissory products: